{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Алгоритм определения пересечения треугольников (пересекаются или нет)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Импорты"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from robobase.trianglealg import *\n",
    "from src.support import drawTriangles"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Тест 1\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Создание треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "tr_one = Triangle([Point([0, 0]), Point([1, 0]), Point([0, 1])])\n",
    "tr_two = Triangle([Point([0.1, 1]), Point([1, 1]), Point([1, 0.1])])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Пересекаются ли?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(tr_one.isIntersection(tr_two))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Отрисовка треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawTriangles(tr_one, tr_two)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Тест 2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Создание треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "tr_one = Triangle([Point([0, 0]), Point([1, 0]), Point([0, 1])])\n",
    "tr_two = Triangle([Point([0.25, 0.25]), Point([0.75, 0.75]), Point([0.6, 0.8])])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Пересекаются ли?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(tr_one.isIntersection(tr_two))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Отрисовка треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawTriangles(tr_one, tr_two)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Тест 3"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Создание треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "tr_one = Triangle([Point([0, 0]), Point([1, 0]), Point([0, 1])])\n",
    "tr_two = Triangle([Point([0, 1]), Point([1, 1]), Point([1, 0])])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Пересекаются ли (касание тоже пересечение)?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(tr_one.isIntersection(tr_two))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Отрисовка треугольников"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawTriangles(tr_one, tr_two)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}